The real world, as
man currently perceives it, is a complex mass of potential stimuli: various
sound and light waves speeding by, molecules of different odors drifting in the
air, hot and cold sources producing varying interactions of temperatures, and
things to be touched, to name just a few. Man is limited by the capabilities of
his sense organs and central nervous system to being able to perceive only an
extremely small percentage of these stimuli. Man’s visual spectrum covers only
a small part of the electromagnetic continuum. Dogs can hear sounds that man
misses, and many animals can smell things that man cannot detect. Other things
cannot be perceived because they are too small, too far away, too fast, and so
forth. Thus man perceives the world through very small windows. He can,
however, enlarge his scope somewhat with mechanical devices such as
microscopes, infrared photography, slow- motion photography, and amplifiers.

Another source of
stimuli is from man’s own body, such as from some internal organs, muscles,
and substances in the blood. Again, whole sources of stimuli are outside of
man’s conscious sensory capabilities. For example, try to perceive the activity
of your spleen.

Given the range of
stimuli that man can potentially perceive, what explains why he receives some
stimuli and not others at any given time? Part of the answer lies in the properties
of the stimuli themselves. Some stimuli simply push their way through the
windows. It is fairly probable that a sudden loud noise or a bright light
will be perceived in some fashion. This chapter, however, is concerned with
the interaction between learning and the sensory-perceptual-attention mechanisms
that select, filter, and interpret the stimuli. Figure 3—1 shows a model of
this interaction. Some of the potential stimuli are selected and perceived
and sent into memory storage. This memory storage then affects the later selection
and interpretation of stimuli.

In the 17th and 18th
centuries, philosophers debating about how our mind comes to have certain
ideas often divided into two camps —
the nativists and the empiricists. Nativists, such as Descartes, argued that man is born with the ability to perceive
certain basic phenomena. The empiricists,
such as Locke and Berkeley,
argued that man learns to perceive. Thus the nativists stressed that
learning depended on perception, whereas the empiricists stressed that perception
depended on learning. Most theorists now reject both of these two extremes
and emphasize the continually evolving interactions between perception and
learning.

There are many examples
of man’s learning to perceive. The first time a person looks through a microscope
at a slide of part of the brain, he perceives very little of what he might
later learn to see in the same slide. The Russian missile
bases on Cuba were discovered by a man who had learned how to identify them
from aerial photographs, whereas other observers of the photograph’s did not
see them (Gibson, 1969, p. 8). The wine connoisseur has learned to perceive
fine discriminations in the taste, smell, and color of various wines.

A key question is
whether learning ever affects the way an object is initially perceived or whether it affects only some later stage in the
processing, of the information obtained from the object. Does the wine actually
taste different to the wine connoisseur, or has he
simply learned to do more (make different responses) to the same taste that
most of us experience? Perhaps learning doesn’t affect the original perception of the object, but rather affects what type of information about the object can be retrieved from short and long term information
storage centers (see Chapter 4). Perhaps these retrieval processes affect what
parts of the information go into the person’s consciousness. Alternatives such
as these may be greatly expanded depending on the number of stages assumed to
exist in the processing of information. The point is that although it is clear
that learning interacts with the information-processing system, it is seldom
clear at what point in this system learning has its effect. One of the early
stages in the system is attention.

ATTENTION

Of all the stimuli a
person is capable of perceiving, he attends to some stimuli at the expense of
others. Considerable research (Norman, 1969, Chap. 2) has been devoted to
determining the variables that affect attention. Consider the “cocktail party
phenomenon.” A room is filled with people talking to each other in small
groups. Thus for any one person there are many voices and sounds impinging on
his ears. Yet he is able to filter out of all this noise the conversation of
the person he is talking to, and selectively attend to this conversation alone.
The person can also easily switch his selective attention so that he tunes in
on the conversation of two people nearby or to the words of the music in the
background. Such selective attention is a truly remarkable process, which, as
Norman (1969, p. 14) points out, has not been duplicated by electronic devices.

Since we can’t attend
to all possible stimuli, it is important that we attend to those stimuli that
are important to us at the time. But how do we know if a stimulus is important
if we don’t first perceive it? Such reasoning usually leads to discussions
of different levels of perception. For example, at a basic, probably non-conscious
level, stimuli are processed and some decision mechanism or filter determines
which stimuli will be further attended to. A different level of perception
then might deal only with these selected stimuli. But on what basis does the
decision mechanism or filter pick some stimuli over others? Part of the answer
is past learning. What is important to the person now usually depends on prior
learning experiences. An experienced automobile driver has learned what stimuli
should be attended to for efficient and safe driving. Thus we see one place
where learning and perception interact: memory storage partially determines
what stimuli will be attended to.

A similar argument
can be made for the phenomena of perceptual vigilance and perceptual defense.
Perceptual vigilance refers to the hypothesis that events of particular
importance to the individual are easier for him to perceive, while perceptual defense refers to the hypothesis that it may be possible
for an individual to not
perceive some events which
are psychologically unpleasant. Although there is controversy about these
phenomena, they may, to some extent, involve processes similar to those described
above for selective attention. That is, some decision mechanism, influenced
by memory storage, must first classify stimuli as important or dangerous before
perceptual vigilance or perceptual defense can occur. Perhaps at one (non-conscious)
perceptual level a stimulus is perceived, interpreted, and classified as dangerous.
On the basis of this classification the stimulus is not permitted into the
perceptual level that involves consciousness, so that the person has no subjective
experience of ever having perceived the stimulus at all.

‘Phenomena such as
hysterical blindness (functional blindness due to psychological rather than
physiological factors) may often be extreme cases of perceptual defense. Consider
the man who has a series of tragic experiences such as the death of his wife
and loss of his job. Many of the stimuli that he encounters, such as things
that remind him of his wife, will elicit excessive anxiety in this man. If
the anxiety is strong enough, the decision mechanism may, via perceptual defense,
stop certain stimuli from entering consciousness. In the extreme, with many
stimuli eliciting an unbearable degree of anxiety, the decision mechanism
may simply shut almost all visual stimuli from consciousness. This, then,
would be a case of hysterical blindness, for although there is nothing anatomically
wrong with the man’s visual system, he is, to a large extent, functionally
blind. Such an explanation is, of course, oversimplified, but perceptual defense
may be a significant factor in cases of hysterical blindness.

The next question is
how learning affects the perception of stimuli that are attended to.

THEORIES
OF PERCEPTUAL LEARNING

From the stimuli
attended to, man constructs some idea of his environment. Although some of the
processes involved in this construction may be innate, many appear to be
learned, and this learning how to perceive is called perceptual learning.

There are basically
two theoretical approaches to perceptual learning. The first emphasizes that
the environment supplies most of the needed information for perception; perceptual
learning is described in terms of learning how to effectively use information
from the environment. (This is the approach of E. J. Gibson.) The second approach sees the environment as supplying inadequate
information, and therefore views perceptual learning in terms of learning
to make extrapolations from this limited information. This is the approach
of the transactionalists and theorists such as Bruner. These two approaches
are, of course, different points along a continuum of theories on how much
information is supplied by the environment. Research will have to tell us
which points on this continuum offer the best explanation of perceptual processes
in different situations.

A few examples of
the two approaches will be given below. (For a more complete coverage see
Gibson, 1969.) Keep in mind that the specific experiments and arguments given
with each theory are not unique to that theory but could be interpreted differently
to fit a different theory.

Gibson’s
Theory. Gibson’s (1969)
theoretical position, a perceptual differentiation theory, is that perceptual learning
is learning to extract information out of the sensory data of the environment.
The environment is seen as supplying an abundance of information. To make sense
of the sensory input a person must learn how to respond to distinctive features of the stimuli. For example, when a young
child first hears people speaking to him it probably sounds like an
undifferentiated mass of meaningless sounds. With time the child learns to pick
out distinctive features, basic sounds that he can use to discriminate words,
meanings, and other aspects of the language.

Perceptual learning,
then, according to Gibson, has two components: First, the person must learn
what the distinctive features are (e.g., according to what criteria does a
good wine differ from a bad wine?). Second, the person must learn how to use
the distinctive features to discriminate different relevant objects. At the
start of a task a person might already be able to identify the distinctive
features. If so, then perceptual learning is facilitated, as the person has
only to learn how to use these features. Memory of an object is conceived
as being stored in terms of distinctive features and invariant patterns, as
opposed to just an unanalyzed copy of the stimuli.

Learning to identify
and to respond to distinctive features involves processes such as abstraction,
filtering out irrelevant variables, and selective attention. Abstraction involves distinguishing common elements or relationships. For example,
in language learning we learn to identify (abstract out) certain basic sounds
independent of differences in pitch, loudness, or speed. Filtering out irrelevant variables is learning to ignore those parts of the stimulation
that are not essential to the required perception, as pitch might be irrelevant
to the understanding of some parts of language. Selective attention, according to Gibson, refers to the exploratory
activity of the sense organs, such as turning the head toward a sound or rolling
a liquid over the tongue.

Let us consider the
implications of Gibson’s theory in a practical situation: teaching. To facilitate
perceptual learning, the teacher should emphasize distinctive features, for
example, through the use of clearly contrasting examples. This technique would
apply equally to elementary children learning to read, to high school students
learning to discriminate among the sounds of different musical instruments,
and to a medical student learning to make sense out of electroencephalogram
records. First the teacher would help the students to identify some of the
distinctive features; for example, pointing out cues for the student to discriminate
between the string instruments. Then through the systematic use of contrasting
examples the students would be given practice using the distinctive features
to make the required discriminations.

Transactional
Theory. An example of a
theory that emphasizes extrapolations from limited information is transactional
theory (Ittelson & Cantril,
1954). Perception is considered to be dependent on the person’s past transactions
with the environment; it is an active process of interpretation of
environmental events in terms of the person’s purpose, values, and past
learning (e.g., expectations and assumptions). A stimulus pattern on the retina
could have come from a variety of different objects; hence there are a number
of different possible “perceptions.” The actual perception that the person has
thus depends on his past learning: how functional and useful were the different
possible perceptions in the past?

A number of impressive
demonstrations were generated in support of transactional theory (Ittelson
& Kilpatrick, 1951). One of the more outstanding of these is the trapezoidal
window. This is a trapezoid frame with window-like panes in it and shadows
painted on it to make it look like a window (a schematic is given in Figure
3—2). When seen in the proper setting a person can perceive the window in
at least two different ways: (1) It could be seen for what it is, a trapezoid-shaped
window; or (2) It could be perceived as a rectangular shaped window seen from
an angle. Because most people’s transactions with windows in the past have
been primarily with rectangular windows, the trapezoid window is generally
perceived as a rectangular window.

Now consider what
happens when the window is slowly rotated about the post shown attached to the
middle of the base of the window. If the person perceived the window as being a
trapezoid, he would simply see a rotating trapezoid. But he perceives the
window as a rectangle. A rotating trapezoid does not produce the same types of
retinal images as a rotating rectangle would. In order to fit the actual
retinal images into the rectangle “hypothesis,” the person’s perception becomes
distorted. What he immediately sees is an oscillating window whose speed,
shape, and direction of turning seems to keep changing. It may seem to slowly
move around in one direction and then to suddenly dart around in the other
direction. If a rod is hung through the window pane, it often appears to move
in a direction opposite that of the window. Sometimes the rod appears to bend,
break, or pass through solid parts of the window; Thus in order to maintain
seeing the trapezoidal window as a rectangle, most people will literally
perceive the window and rod as doing things that they know are impossible. For
instance, this author always sees these perceptual effects even though he
“knows” the window is a trapezoid and “knows” it is turning at a constant rate.

The transactionalists
have made a good case for the influence of learning on perception. However,
as Gibson (1969, p. 45) pointed out, they have not included a clearly formulated
explanation of how this learning takes place.

Categorization.
Some theorists, such as
Bruner (1958), have conceived of perception as influenced by categorization.
The categories by which things are classified are generally a result of
learning. Thus when some object or event is first perceived it is classified
according to a system of categories. The final perception then depends on how
the sense data were categorized at the time they were first perceived. Any
difference between the actual object or event and the category under which it
was classified may result in a distortion in the perception of the object or
event, in order to make it fit the category. This “distortion” may often be
nothing more than the selective perception of some features of the object over
other features. Also, differences between categories and environmental events
may produce modifications of the categories.

Consider playing
cards in which the colors of the suits are reversed: spades and clubs are
red, while hearts and diamonds are black. These cards do not fit into the
card categories of an experienced card player. If quickly shown a red ten
of spades, the card player’s perceptual system tries to map the sense experience
into a category such as black spades, red hearts, or red, diamonds, but there
is no category for red spades. Thus the person might misperceive the red ten
of spades and perceive it as the black ten of spades or the red ten of hearts.
In an experiment with such cards, Bruner and Postman (1949) identified two
frequent types of errors: dominance reactions and compromise reactions. A
dominance reaction occurred when the subject forced the suit to
match the color or the color to match the suit. Perceiving the black five
of hearts as the red five of hearts is a dominance reaction. In a compromise reaction the subject perceives some compromise between
the actual object and a dominance reaction. For example, a red spade might
be perceived as a purple spade (purple being a compromise between red and
black), or as a black spade with red edges.

The author has introduced
such a color-reversed deck into bridge games with experienced bridge players.
The usual response by the players when they first pick up the cards is that
there is something funny or unpleasant about the cards, but they don’t know
exactly what it is. Many players play an entire hand without seeing what is
actually different about the cards. Such players often have trouble sorting
the cards into suits, suddenly noticing that they have five suits in their
hands. Even when the color reversal is noticed, following suit during the
play of the hand is often difficult. This author still gets a slightly unpleasant
feeling from looking at such cards as a black jack of diamonds. On the other
hand, the author’s wife, who does not play cards (and hence does not have
as set a group of categories) never had any trouble immediately seeing the
cards as they actually were.

As another example
of categorization, consider prejudice. The roots of prejudice are many and
varied. Aronson (1972, p. 180) lists four basic causes of prejudice: (1) economic
and political competition or conflict, (2) displaced aggression, (3) personality
needs, and (4) conformity to existing social norms. From the perspective of
the preceding discussion it could be argued that an important variable in
many cases of prejudice is the type of categories the prejudiced person uses
in perceptually classifying people.

Consider a person
with the following three categories for automobile drivers: good male drivers,
bad male drivers, and bad female drivers. Now assume that a female driver
passes by this person, but so fast that he doesn’t get a clear look at the
sex of the driver. Assume also that the female driver displays particularly
good driving skills. Because our prejudiced person has no category for good
women drivers (she’s a black queen of hearts in his world), he misperceives
the situation and perceives the driver as being male (a dominance reaction).

We can see how such
a categorization argument can be applied to many forms of prejudice. Bruner
(1958, p. 86) suggests. “We see a Negro sitting on a park bench, a Jew or
Texan changing a check at a bank window, a German dressing down a taxi cab
driver, and allocate each experience to an established and well-memorized
stereotype: lazy Negro, mercenary Jew, rich Texan, bullying German.” Now there
are many reasons why a black might be sitting on a park bench, few of which
are because he is lazy. Perhaps he is on a break from a ten-hour-a-day job.
But if the prejudiced person has only one category for blacks, a category
that includes being lazy, then the prejudiced person’s perception of the black
may be distorted. Worse still, when this person remembers the scene of the
black in the park, his memory includes all of the distortions he originally
added to the perception. Such a person perpetuates his own prejudice because
his misperception and distorted memories are proof to him of the validity
of his stereotypes.

This type of distortion
is illustrated in an early study that Allport and Postman (1945) did on rumors.
(Please remember that the study is 30 years old, and some of the specific
findings might be different today, although the psychological processes are
assumed to be the same.) In their study they would show one subject a picture
which he would describe to a second subject, who then told a third subject,
and so forth. This way the experimenters could observe the types of changes
that took place as the story was passed on. Some of the results could be interpreted
in terms of our categorization model. One picture was a subway scene that
included a white man holding a razor while arguing with a black man. In over
half of the final stories the black ended up holding the razor, Perhaps for
many of the people a razor during an argument better fit the black category
than the white man category. In some cases the number of blacks increased
to four or “several.”

Although the preceding
examples might be fairly extreme, it can be argued that everyone has a limited
number of categories and so must be misperceiving some events. One purpose
of education then is to increase the number of categories a person has and
uses in order to decrease the amount of misperception.

The Hebbian Model. Hebb (1949) offered a provocative theory of perceptual
learning in his book Organization
of Behavior, The theory
suggests that there are neural representations that correspond to environmental
stimuli, and that learning involves neuronal associations between such
representations. According to Hebb, simple visual perception can be broken down
into small units such as lines and angles. With learning, these basic units
form into simple figures and then into more complex perceptions. Hebb explains
this learning in terms of associations between neural units. For example, one
set of neurons might respond to a particular angle, while another set responds
to a particular line. The perception and memory of a figure that includes this
angle and line require a learned association between the two sets of neurons.
In other words, we start seeing very simple things and gradually learn to be
able to see more complex perceptions.

In developing his
theory, Hebb drew heavily on the work of the German ophthalmologist von Senden
(1960). Von Senden studied adults who had been virtually blind since birth
and then were suddenly given sight by an eye operation such as removal of
cataracts. Hebb distinguished two processes of perceptual development: figural unity, the simple detection of an object against its
background, and identity,
identifying an object as a member of class of
objects. Von Senden’s patients, when given sight, were generally capable of
figural unity, but seldom capable of identity. They could fixate on objects
and follow moving objects with their eyes, but at first could not identify
objects. In the beginning the patients relied a lot on color. If the shape
of an object were changed but the color left the same, the patients often
still identified the object as being the same.

Although von
Senden’s subjects could detect a square or triangle against its background,
they could not at first tell one from the other unless they counted the number
of corners. Similarly they could not tell which of two sticks was longer unless
they felt the sticks. Even when they learned to identify some objects by sight,
a change in the physical orientation of the object might make it unknown again.
With time the subjects learned how to visually identify more and more objects,
but for many of the subjects their visual skills never became “normal.” Two
years after the operation one patient could identify only four or five faces.

Hebb argued that
the type of perceptual learning seen with von Senden’s subjects corresponded
to what occurs with normal infants. The advantage of von Senden’s subjects
is that they could verbalize what was taking place. Although correspondences
between von Senden’s patients and infants may exist, there are too many differences
to enable us to draw any firm conclusions. For example, von Senden’s subjects
may have experienced some side effects when suddenly given sight (e.g., dazzle
of bright lights or cramps in eye muscles) that impaired their visual progress,
which wouldn’t be the case in a normal infant. It should also be remembered
that von Senden’s subjects had spent their entire lives learning to interpret
the world through sense modes other than vision. Thus it would be expected
that these other learned responses related to handling the environment would
interfere with the acquisition of new visual responses. In fact, many such
patients often prefer their old sense modes to their now confusing vision.
Another possibility is that there are certain critical periods in
human visual development similar to the critical period discussed in the first
chapter regarding imprinting. That is, there might be certain critical periods
in the development of an individual in which he is particularly predisposed
for some type of learning, such as perceptual learning. If this critical period
is bypassed, as with von Senden’s subjects, the learning may be significantly
more difficult,

Later support for
theories such as Hebb’s came from the neurophysiological studies of Hubel
and Wiesel (Hubel, 1963). Using recording electrodes in the visual cortex
of cats (striate area of the occipital cortex), they studied what types of
external visual stimuli would cause different nerve cells to fire. They found
some cells that maximally fired to simple lines presented at one orientation
to the eye, while other cells fired maximally to lines at other orientations.
Some cells fired to movement of a stimulus in one direction in the visual
field, but not to movement in the other direction. This type of cell function
appears to be innate in that it can be demonstrated in newborn kittens.

Hubel and Wiesel
.categorized the nerve cells they studied into two basic groups: simple cells
and complex cells. Simple
cells are ones that respond
only to line stimuli of a specific orientation and position, while complex cells are more general in what they respond to. Hubel
and Wiesel suggest that complex cells receive input from a number of simple
cells.

For example, one
simple cell might respond only to a dark vertical line in a specific part
of the left visual field, and another simple cell might respond only to a
dark vertical line in part of the right visual field; whereas a complex cell
that receives input from these two simple cells might respond to the vertical
line in either place. With more simple cells feeding into a complex cell we
can imagine a complex cell that responds to a vertical line anywhere in the
visual field. As complexity of the cell increases, we might find a cell that
fires to figures of triangles if it gets the right input from cells that respond
to horizontal lines plus cells that respond to slanted lines of a certain
orientation.

It is easy to see
how we could slowly build up a model of vision this way with more and more
complex cells. Such a model could be compatible with Hebbian theory, as Hebb
also sees perceptions building up from simple components such as those of
simple cells. Such models, however, go far past the basic findings of Hubel and
Wiesel, and hence should be considered quite speculative. We also have to be
careful to not construct a model of perception that simply provides
firing neurons to correspond to visual stimuli. A model of perception must be
more flexible in order to include such intricate phenomena as visual illusions
and the effects of learning on the interpretation of sensory events.

VISUAL ILLUSIONS

Gregory (1966, Chap.
9; 1968) has shown how past learning might account for a number of visual
illusions. Consider the illusions given in Figure 3—3. In the Ponzo illusion the top of the two horizontal lines usually
looks longer, although both lines are actually the same length. In the Müller-Lyer illusion the shaft of the first “arrow” with the ends
turned out usually appears longer than the shaft of the second arrow with the
ends turned in.

Gregory suggests
that both illusions suggest depth to the viewer, and that those features of
the figures “assumed” to be more distant appear larger. The Ponzo illusion
corresponds to experiences the viewer has had with similar figures, such as
railroad tracks. Our experience with railroad tracks has taught us that when
our eye gets the image of the Ponzo illusion, the top of the figure is actually
farther away from us than the bottom. Hence the top of the two horizontal
lines is farther away, but both lines produce the same sized image on the
retina. If two objects produce the same sized retinal image and one object
is farther away, the farther object must be larger, and often will appear
larger. So the argument is that somewhere in the perceptual processing of
the Ponzo illusion the actual retinal images are compared with information
about how far away the different parts of the figures are, and the results
of this comparison determine our subjective experience of the relative sizes
of the different figure parts. And it is past learning that affects the distance
estimation.

Similarly the Müller-Lyer
illusion might be explained in terms of our past experience with corners of
rooms and buildings. If you look at the inside corner of a room, the line
edges formed by the walls, ceiling, and floor, you will see a three-dimensional
representation of the MullerLyer arrow with the ends turned out. Note that
in this situation the shaft of the arrow is the part farthest away. Now if
you look at the outside corner of a flat-roofed building, such as a phone
booth, the line edges form a three-dimensional example of the arrow with the
ends turned in. Here the shaft is the nearest part of the figure.
If we look at the illusion, both shafts produce the same retinal image. But
the one shaft is “assumed” to be the nearest part of the figure, while the
other shaft is “assumed” to be the farthest part. Therefore we perceive the
farther shaft to be larger.

If these illusions
can be explained by the depth they suggest, why don’t the illusions look more
three-dimensional? This is probably because the figures are printed on paper
which superimposes a two-dimensional effect, but not a strong enough effect
to offset the illusion. If instead the Müller-Lyer figures are constructed
out of wire, painted with luminous paint, and viewed with one eye (to avoid
stereoscopic depth information) in the dark, then they do look three-dimensional,
like corners.

We have now seen
a number of ways in which learning might affect perception. The next question
is how the particular language a person learns affects his perception.

THE WHORFIAN HYPOTHESIS

The Arabs have a
multitude of different ways of naming camels, and the Hanunoo people in the Philippines
have names for 92 different varieties of rice (Bourne et al., 1971, p. 285). Do
these languages affect the way the person actually perceives and thinks about
his environment? In other words, does learning a language affect later
perception of the world? Is the Arab’s perception of camels different from
ours, or does he just use the available information differently?

Whorf offered an
interesting theory relative to these questions, called the Whorfian hypothesis or the linguistic relativity hypothesis (see Bourne et al., 1971, Chap. 13; Carroll, 1956). According to this
theory, language is not simply a medium of communication and thought. In addition
to these generally accepted functions of language, Whorf contends that the
structure and semantics of any particular language mold the way a person perceives,
understands, and responds to his environment. Similar to the process of categorization,
language provides a framework for the
person’s perception and storage of information. According to Whorf we dissect
nature along lines laid down by our native language.

For example, Whorf
noted that English grammar tends to divide sentences into noun phrases and
verb phrases. He suggested that an effect of this grammar was that English
speaking people have a tendency to analyze all of their experiences in terms
of one of two categories — things or actions. (If your counterargument
is that this is the only or best way to divide experiences into categories,
then you are proving Whorf’s point.) Whorf spent considerable time studying
the Hopi Indians, and many of his examples come from these studies. It appeared
to Whorf that the Hopi did not have tenses for their verbs. From this Whorf
concluded that the Hopi perception of the world must then be timeless. The
Hopi language also had no word for imaginary space, which suggested to Whorf
that the Hopi could not even imagine something like a missionary’s hell. However,
many of Whorf’s conclusions about the Hopi have since been questioned.

Although the Whorfian
hypothesis may be true to some extent, there are too many confounding variables
to determine its exact status. Consider the problems in showing how language
affects thinking. First of all, most of what we know about another person’s
thinking processes comes from what the other person tells us, and so we are
using the person’s language
as a measure of the effects
of language on thinking. This boils down to showing the effects of language
on language, which isn’t too revealing. Secondly, there is the complication
that most of thinking revolves around language. (Try to “think” about some
topic without using words.) Therefore language must affect thinking, since
it is one of the components of thinking.

But our question
is whether language affects perception.
Does one of the Hanunoo
people, who can discriminate 92 varieties of rice, literally see rice differently
than we do? Perhaps not. It may be that the importance of rice to these people
plus their greater experience with different types of rice simply allows them
to make more and finer judgements. The fact that they have more words for
rice than we do in English simply reflects their ability to make more discriminations.
That is, rice “looks” the same to them as it does to us, but they know more
things to look for and have more words to classify what they see.

Thus one of the Hanunoo
can code in one word (one of the 92 varieties) a lot of information about
the rice. When he tells one of his friends which variety of rice he is dealing
with, considerable information is exchanged. An English speaking person who
knew what to look for might be able to code the rice with all of the same
information, except that instead of a single word, his identification of the
rice might involve a number of short descriptive phrases. One-word coding,
being more efficient after you have mastered the coding process, may facilitate
learning, remembering, and thinking about rice. But that is quite different
from saying that the original perception is affected, So at this time we can’t
say whether language affects original perception and/or only affects other
processes, such as how the information is coded. The distinction between perception
and coding is also far from clear.

In a relevant experiment,
Brown and Lennenberg (1954) categorized a set of color discs according to
“codability.” A color disc with a high codability score was one which most
of their subjects gave the same name (e.g., “red”), while a disc with a low
codability score was one given many different names and descriptive phrases
(e.g., “dirty reddish green”). Next, a different set of subjects was shown
some of the individual discs and had to match each color with one of the discs
from a large display of all the color discs. When these subjects had to find
one disc at a time, it made no difference whether the disc was of high or
low codability. However, when the subjects were asked to find
four discs simultaneously, they were faster and more accurate at finding discs
of high codability. When looking for just one disc the subjects simply kept
a picture of the disc in their mind as they scanned the display of discs.
Doing four at a time, however, depended more on how the subjects coded the
colors.

Gibson’s (1969) position
on language and perception is that the person first learns to perceive objects
and their features and later learns names for these objects, as opposed to
Whorf’s position that language affects the original perceptual development.
Gibson questions whether perceptual learning is appreciably affected by language
categories, although she does allow that perceptual learning can be facilitated
by calling attention, as with language, to distinctive features of the objects.

SET

The discussion in
this chapter has shown that stimuli do not fall on passive receivers. Rather,
each person is predisposed to perceive stimuli in specific ways. This
predisposition has sometimes been referred to as set, an ambiguous, generic term that encompasses a range of variables,
including past experiences, motives, context, rewards, and instructions (see
Dember, 1960, Chap. 8; Haber, 1966). In fact, the concept of set includes
everything discussed so far in this chapter.

Think about the answer
to the following question before reading further: Why are 1972 pennies worth
almost twenty dollars? The answer is that you need two thousand pennies for
twenty dollars and so you are only twenty-eight cents short. Most people have
some trouble with this question because they are in the set of perceiving
1972 as a date, not as a number of pennies. But is this effect really on the
perception of the 1972 or is it on some other stage in the processing of the concept
of 1972, or possibly both? This is a key, and as yet unanswered, question.

The next example
will illustrate how set-like effects can affect problem solving processes.
Quickly answer the following question before reading further: If polk is pronounced poke with the l silent, and folk is pronounced foke, also with a silent l, how do you pronounce the white of an egg? Most people who answer quickly
do not realize the white of an egg is the albumen, not the yolk, for they
are in the set of words ending in lk or ke.

Figure 3—4 gives
two other examples of set. Read each example fairly quickly and then go back
and look for the set. The following are the answers for those who want them:
In the first example the word “the” is printed twice, but most people read
over one of the “the’s” because “bird in the hand” is such a common phrase.
In the second example the last word may be read as a Scottish name because
of the set established by the first three words, but it can also be the common
word “machine.”

Let us now consider
how set can influence people’s perceptions in a classroom situation. Kelley
(1950) used students in a college psychology class. The experimenter came into
the class one day and told the students that their regular instructor was to be
replaced by a substitute for the day. The students were then given a written
description of the substitute. However, the descriptions were not all the same.
Half of the descriptions referred to the substitute as being a rather cold
person, while the other half were the same except the word “warm” was used
instead of “cold.” During the class that the substitute taught, the students
who got the “warm” description participated more in class discussion with the
substitute than did the students who got the “cold” description. After the class,
the “warm” description students evaluated the substitute higher than did the
“cold” description students, in terms of being more considerate, better
natured, and so forth. All students were in the same classroom with the same
substitute, but, according to the set they were in as a result of the written
descriptions, they perceived the substitute differently and interacted with him
differently. The substitute’s actual personality and teaching style, of course,
affected the students’ ratings and perceptions in an absolute sense, but set
made the difference between the two groups of students. It is easy to see how
this type of phenomenon takes place all the time in classrooms, as students
tell other students what they think or have heard about a particular teacher.
Similarly, people perceive and respond differently to political figures (or
anyone else for that matter) and to their speeches, depending on their
particular “set.”

Set also affects
a teacher’s perception of his students. Rosenthal and Jacobson (1968) told
elementary school teachers that they had a test that would identify “spurters”
— sudden
fast learners. In fact, the students they identified as spurters were chosen
randomly; thus any difference between spurters and non-spurters was purely
due to the set of the teachers’ minds. Over time, the “results indicated strongly
that children from whom teachers expected greater intellectual gains showed
such gains.” The teachers also described the spurters as being happier, more
curious, more interesting, more appealing, better adjusted, more affectionate,
less in need of social approval, and as having a better chance of being successful
in later life. The non-spurters also improved intellectually, but the more
they improved, the less favorably they were rated by the teachers. This effect
of set was powerful, particularly on first grade teachers.

How did the teachers’
sets influence the students’ intellectual gains? Rosenthal and Jacobson argue
that it was not simply that the teacher spent more time with spurters but that the effect lay inmore
subtle interactions: “Her tone of voice, facial expression, touch and posture
may be the means by which —
probably quite unwittingly — she
communicates her expectations to the pupils. Such communication might help
the child by changing his conception of himself, his anticipation of his own
behavior, his motivation or his cognitive skills.”

Other researchers
(e.g., Clairborn, 1969) have criticized the methodology of Rosenthal and Jacobson
and have failed to replicate their findings. O’Leary and Drabman (1971) conclude
that “At most, the evidence

demonstrating
such an effect is equivocal, and the teacher’s behavioral changes that result
from receiving a ‘false’ expectation are quite variable.” However, Rosenthal
and others (Rosenthal, 1973) have demonstrated this type of phenomenon in
enough situations that it appears to be a legitimate and powerful effect in
at least some circumstances.

Keeping Rosenthal
and Jacobson’s theory in mind, consider what might be happening to students
classified as slow learners, problem students, or special education students.
Suppose a student does something that could be perceived as either creative
curiosity or bothersome digression. The “spurter” might be rewarded and
encouraged, whereas if the teacher is in the set of thinking of the student
as a problem student, the student might be verbally punished and discouraged.
What a shame this would be if the behavior had elements of curiosity that
could have been encouraged.

Those who work
with teacher training try to minimize such effects of set by discouraging
the labeling of students and encouraging well-specified behavioral objectives
and systematic keeping of behavioral records. That is, if the teacher decides
exactly which behaviors should be encouraged and which should be discouraged
(regardless of who does them) and how he can objectively determine
which kind of behavior has occurred in a given situation, then the effects
of set will be dramatically reduced.

Many clinicians
utilize projective tests
to aid in the personality assessments of their
clients. These tests consist of relatively unstructured stimuli that the
client must organize or interpret in some way. For example, the person might
be shown an inkblot and asked to tell what it looks like. The assumption
is that the responses that the subject makes to the projective test are
some measure of his personality. Unfortunately such responses might be affected
by set. The clinician could, although subtly and unintentionally, influence
the subject’s set so that the subject will respond to the projective test
in ways that fit the clinician’s expectations or theoretical bias. It is
possible that many clinical phenomena, such as the types of symbols that
a person reports as having occurred in his dreams, are influenced by set.

As mentioned earlier,
we cannot at this time say for sure whether set affects the original perception
or some later stage of information processing, or perhaps both. Haber (1966)
discusses two contradictory hypotheses about set: (a) set affects the percept
of the stimulus while the
person is actually viewing it, and (b) set affects the report of the stimulus
without affecting its percept. Haber summarizes as follows:
“This review must conclude inconclusively with respect to a choice between
the two hypotheses. Some evidence exists to support each of them, and some
exists which favors one over the other. But there is none that supports
one while disproving the other.” It does appear, however, that learning
is a major variable affecting set, and that set may affect perception. This
suggests again the possibility that learning plays a role in perception.
Next we will consider how learning and perception interact in the learning
of verbal material.

PERCEPTION
AND VERBAL LEARNING

Different types
of verbal material vary in their ability to elicit images. Does this image-eliciting
ability affect how easily the material can be learned and remembered? This
is the question investigated by Paivio (1969). Paivio classified verbal
stimuli along a dimension from concrete to abstract. A concrete stimulus,
such as the word “house,” is more likely to evoke images than an abstract
stimulus such as the word “truth.” (Say each of these words to yourself
and see which elicits more images.) Paivio suggests that concrete stimuli
derive their meaning through association with specific objects and events
as well as through association with other words. Learning of concrete material,
then, could utilize the images or verbal associations, or both. Abstract
stimuli, on the other hand, derive their meaning largely through associations
with other words. Thus learning of abstract material would primarily utilize
verbal associations.

Paivio often used
a form of paired associate
learning of noun pairs.
Paired associate learning involves presenting the subject a number of paired
items that the subject must learn to associate together. In Paivio’s task
the subject, when presented with the first noun of a pair (the stimulus),
had to learn to say the second noun (the response). Paivio found that learning
was faster if the stimulus noun was concrete than if it was abstract. For
example, it is easier to learn an association to the word “house” than to
the word “truth.” Paivio suggests that images serve as “conceptual pegs”
to which responses can be conditioned. That is, concrete nouns elicit more
images than abstract nouns, and these images form the basis for learned
associations. If given the pair “house —
dog” the subject can easily conjure a scene
involving a house and a dog, which facilitates learning and/or memory. It
is not so easy with the pair “truth —
dog.” Thus a person learns
images to certain stimuli and these images facilitate later learning that
involves the stimuli.

Paivio showed that
the effects of noun-imagery were greater on the stimulus side than on the
response side. Although having a concrete response noun might yield better
learning than an abstract response noun, it is more important to have a
concrete stimulus noun. The effects of noun-imagery were also found to be
relatively independent of how meaningful the material was to the subject,
i.e., how many associations the subject already had to the specific material.
Although meaningfulness and noun-imagery often go together, in Paivio’s
tasks the imagery had a greater effect on learning.

The rest of this
discussion of verbal learning centers on how the subject encodes stimuli.
Whether or not this should be called “perception” depends on the definition
of perception. This author includes under perception all processes of cataloguing information. Memory and retrieval processes begin
after this. Others have defined perception so that its domain stops earlier
in the information processing.

Consider paired
associate learning in which the subject is presented with one or more pairs
of items and must learn to associate the members of the pair. Paivio’s task
above was a form of paired associate learning. Another task might have pairs
such as LUF—ZIJ, where the subject must learn to make the response ZIJ
when presented with the stimulus LUF. A critical
part of paired associate learning must be learning to tell the stimuli apart,
a process called stimulus
discrimination. Early
theories of paired associate learning (e.g., Gibson, 1940) emphasized the
role of stimulus discrimination. Now this emphasis has shifted somewhat
to how the subject encodes the stimuli.

According to stimulus encoding theory the subject translates stimuli into forms that
are easier to use in the current task. A visual stimulus might be coded
into a verbal phrase and stored verbally. Or the stimulus LUF might be encoded
as “love” for easier processing. Martin (1971) has argued that “a major
portion of learning is perceptual learning—learning an effective identifying
response to the nominal stimulus situation.” Let us say that a person learns
one set of paired associates and then has to learn another set which consists
of the same stimuli but different responses. (This two-stage learning is
referred to as the A-B, A-C paradigm.) According to Martin the subject in
learning to do this might learn to code the stimulus differently the second
time; that is, he might make a different identifying response to the same
stimulus.

COMPLEXITY

Perception, learning,
and motivation all come together in explanations for animals’ (including
humans) apparent need for sensory complexity. Animals strive for variety,
novelty, and complexity in their environments even though such striving
does not seem to serve any immediate biological need. There is a vast literature
on such phenomena (Berlyne, 1960; Dember, 1960, Chap. 8; Eisenberger, 1972;
Fiske & Maddi, 1961), which includes the following examples. Bees prefer
those flower shapes with the longest outline with respect to surface area.
Some fish will learn mazes just to look in a mirror. In mazes with many
correct paths to the goal, rats vary their paths and often prefer those
paths with greater variety. Rats will also learn a simple maze where the
reward is the opportunity to explore another maze. A rat will press a bar
simply to turn a light on and then will press another bar to turn it back
off again. Monkeys like to handle objects, and show preference for the more
heterogeneous objects. Monkeys will also open windows or pull levers to
see outside their cages, and will keep doing this, particularly if the environment
keeps changing. Coming home from work, a man might decide to take a different,
perhaps even longer route, just for a change in his routine. Women rearrange
their living room furniture for similar reasons. Teachers find that almost
any significant change in the classroom (painting the walls, installing
new blackboards, introducing a new audio-visual device, making new seating
arrangements) seems to improve learning for a while. In Chapter 7 we will
see how need for variety might be a major personality variable.

Phenomena such
as those just noted have been described under many names: exploration, novelty,
curiosity, stimulus change, and stimulus satiation. Is there something common
to all these phenomena, some theoretical construct under which they all
fall? One answer to this question centers on stimulus complexity and animals’
attempts to experience a certain degree of complexity: a novel stimulus
is often more complex than a familiar stimulus. Exploration and curiosity
are simply attempts to increase the stimulus complexity of the environment.
Unfortunately there is no good independent measure of complexity, although
there have been attempts to measure it in terms of information theory, conflict,
or specific stimulus attributes. A useful way of thinking about complexity,
following Dember (1960, p. 352), is that “the more complex stimulus is the
one the individual can do more with: it affords more potential opportunities
for responding than does the less complex stimulus.” Most experiments, however,
simply use stimuli that intuitively differ in complexity. We are nowhere
near the point where we can take any two stimuli, particularly if they are of different sense modalities, and say
that for a given organism one stimulus is more complex than the other.

To explain the
effects of stimulus complexity many theorists use the concept of arousal.
Arousal is a general excitatory process—a nonspecific drive — perhaps
related to the activity of the reticular formation, a neural system in the
brain stem. Although most studies measure arousal in terms of some physiological
phenomenon (skin resistance, pupil size, EEG, heart rate, respiration, blood
pressure), there is poor correlation among changes in the various measures.
This raises the questions of which measure is best and whether there is
more than one type of arousal.

Many theories have
been offered to interrelate complexity, arousal, and behavior (see Eisenberger,
1972). A few of these will be mentioned under the following categories:
minimum arousal theories, arousal induction theories, and optimal arousal
theories.

Minimum Arousal
Theories. The
general orientation of minimum arousal theories is that arousal is a measure
of deviation from an optimal state, so the less arousal there is, the better.
Arousal might be produced by many different variables, including states
of deprivation and noxious stimulation. Malmo (1958) suggested that drives
could be broken down into general arousal plus a directional component.
Since reduction in the drive was considered by Malmo to be rewarding, Malmo
would be a minimum arousal theorist.

Dember (Dember,
1960; Dember & Earl, 1957) describes a pacer theory of complexity
that doesn’t mention arousal per se, but that can be considered akin to
arousal theories. According to this theory each animal has a preferred level
of stimulus complexity. The animal will seek out that stimulus situation
whose complexity is near his preferred level. Being forced to attend to
stimuli that are too complex, or not complex enough, causes emotional disturbance.
A pacer is a stimulus whose complexity is slightly higher than the animal’s complexity
level. When an animal interacts with a pacer the preferred complexity level
of the animal moves toward that of the pacer. Thus the animal’s complexity
level keeps rising as its experience with pacers increases.

A baby’s complexity
level is very low at first. He is very content with low complexity stimuli
that might bore an adult, and can be overwhelmed by fairly complex stimuli
that are pleasing to an adult. To “protect” himself, the baby might not
attend to complex stimuli or he might screen them out early in perceptual
processing. As the baby grows and encounters pacers, his complexity level
rises and he seeks stimuli of greater and greater complexity.

Rather than speaking
of a single complexity level for each animal, Dember suggests that there
may be different complexity levels for different types of stimuli. Thus
a person’s complexity level for music might be significantly higher than
his complexity level for literature, probably because in his life he has
had more experience with music and thus encounters more music pacers.

Pacers are usually
pleasing because they are different enough to be not boring, but are not
so complex that they are disrupting. Humor often involves situations that
are somewhat unexpected, but not so strange that we strain to make sense
of them. Beethoven is said to have remarked that in music everything must
be at once surprising and expected.

Although pacer
theory emphasizes the continual rise in complexity levels, one wonders if
complexity levels ever decrease. Are there negative pacers which when encountered
lower the animal’s complexity level? It may be that although an animal’s
complexity level along some dimension generally rises, it does fluctuate
back and forth, including many short term decreases.

If we assume, as
some theorists after Dember have done, that discrepancies between an animal’s
complexity level and the stimulus complexity produce arousal, then pacer
theory is a minimum arousal theory, for animals work for minimum discrepancies
between their complexity level and that of stimuli.

Pacer theory assumes
that if the complexity of the stimulus situation is too far from the animal’s
preferred level, there will be emotional disturbance. Chimps are frightened
by a model of a chimp’s head without a body. They have always seen heads
on bodies; a head without a body is too complex. Human infants are often
distressed if they hear a strange sound coming from a familiar face or a
familiar voice coming from a strange mask. Many unpleasant experiences with
hallucinogenic drugs such as LSD result from the person’s being overwhelmed
by sensory and thought experiences unfamiliar to him.

The effect of stimuli
of too little complexity may be simply boredom. In more extreme situations,
such as sensory deprivation, the effects are more pronounced. Sensory deprivation is not so much the depriving a person of stimuli
as it is a drastic reduction in stimulus complexity. In some of the first
studies (Bexton, Heron, & Scott, 1954), college students were paid to
stay in a room lying in a bed. To reduce visual complexity their eyes were
covered with translucent goggles. Auditory complexity was reduced by the
person keeping his head in a U-shaped foam rubber pillow and hearing the
hum of the air conditioner. Tactile complexity was reduced by having the
person wear cotton gloves and cardboard cuffs that extended beyond the fingers.
The students stayed this way 24 hours a day with time out only to eat and
go to the toilet. Most subjects lasted only two to three days, although
a few were able to last longer.

The effects of
sensory deprivation on the subjects were quite disturbing. The subjects
were restless, often displaying constant random motion. Their feelings vacillated
between anger and mirth. Perhaps most distressing was that they could not
think clearly for any length of time. It appears that the functional organization
of the mind that we call rational thinking requires a certain amount of
environmental support in the way of stimulus complexity. The subjects also
had hallucinations, similar to drug-induced hallucinations, that ranged
from simple objects to complex scenes, such as rows of yellow men with caps
on or squirrels with packs on their backs marching purposively along. The
hallucinations weren’t totally visual but also often included specific voices
or sounds, and specific feelings. This seems to be the same type of phenomenon
as the hallucinations reported by aviators during long flights, by truck
drivers during extended trips, and by radar screen watchers whose shifts
are too long. The explanation is probably that when the environment does
not provide enough complexity, the mind draws from other sources.

While some of the
subjects were in sensory deprivation they heard a recording of a talk arguing
in favor of the existence of ghosts and supernatural phenomena. For some
reason the talk in this situation was particularly persuasive. Some of the
subjects said that for days after they left the sensory deprivation they
were afraid they would see ghosts. This finding suggests that sensory deprivation
might be a powerful influence technique, but almost no one has seriously
investigated it. Adams (1965) put a hospitalized mental patient in mild
sensory deprivation and presented to him a taped message discussing his
particular case and the aims, procedures, and rationale of psychotherapy.
Adams reports that this message resulted in general improvement in the subject.
Part of the effect of sensory deprivation is that it focuses the subject’s
attention on the message, minimizing most sources of distraction. Unfortunately
Adams had only one subject in this report and the results are confounded
with other parts of the treatment program.

Arousal Induction Theories. There are no true arousal induction theories
— theories
that argue that animals seek to experience the most arousal they can. Such
a theory would be at variance with too much data. But several theories are
almost arousal induction theories. Some theorists, such as Sheffield (see
Chapter 6), argue that stimuli that elicit arousal have a greater determining
effect on the animal’s behavior. Thus the animal may often behave in ways
that, at least temporarily, raise his arousal, for the stimuli that increase
arousal have a greater effect on the behavior than other stimuli. This will
be explained in more detail in Chapter 6.

Miller (1963) suggested
that there are one or more go-mechanisms in the
brain which intensify ongoing responses to cues and traces of immediately
preceding activities. These go-mechanisms are activated by events such as
rewards, drive reduction, and the removal of discrepancy between intention
and achievement. Thus if a rat learns to press a bar when a light comes
on in order to get food, the reward of the food activates a go-mechanism
which intensifies the response of pressing the bar to the cue of the light.
Miller also suggested that the go-mechanism can become conditioned to the
occurrence of the response so that future occurrences of the stimulus will
elicit both the response and the excitatory state. If one equates the excitatory
state of Miller’s go-mechanism with arousal, then Miller’s theory comes
close to being an arousal induction theory.

Optimal Arousal Theories.According to optimal arousal theories there
is a level of arousal that is ideal for each animal. If its arousal is too
low, the animal will seek stimuli to increase arousal, whereas if arousal
is too high, due to fear or hunger, for example, the animal will try to
do what is necessary (e.g., flee or find food) to reduce the arousal to
its optimal point.

Walker (1964) suggested
a theory stating that the more complex a stimulus is the more arousal it
elicits. An organism seeks those stimuli that elicit optimal arousal. However,
according to Walker, as an animal experiences a stimulus, there is a decrease
in the complexity of the stimulus relative to the animal. Thus stimuli that
may have maintained optimal arousal for a while lose complexity, and the
animal seeks out other stimuli.
(In pacer theory the complexity of stimuli stays
the same, and it is the animal’s complexity
level that changes. In Walker’s theory the animal’s complexity-arousal level
is constant while the complexity of stimuli changes.)

Walker equates
the effects of rewards with arousal. This yields the following interesting
prediction: “The rewarded event should undergo progressive and selective
reduction in psychological complexity. Eventually it should reach a level
of psychological complexity that is lower than that of the unrewarded alternative.”
That is, rewards affect behavior as they do because of their effects on
arousal. But as the animal encounters the same reward for the same response
over and over again, the reward reduces in complexity, thus reducing its
effect on arousal. If this were continued long enough the animal should
abandon the rewarded response in favor of another response — probably
non-rewarded — that produces more arousal. Most experiments,
however, are terminated long before this point would be reached and observed.
Unfortunately, there is little research on this prediction of the theory,
although Walker does give some suggestive data. Partial support of the prediction
occurred in an experiment (Walter & Mikulas, 1969) in which rats were
tested each day for over 5 months in an operant chamber where they pressed
a bar for food. The rates of bar-pressing decreased over time, which would
seem to indicate that the food had lost some of its rewarding value.

Berlyne (1967)
has offered an optimal arousal theory in which variables such as novelty,
complexity, and ambiguity produce conflict, which in turn produces arousal.
For example, seeing a novel stimulus, such as a picture of a dog’s body
with a bird’s head, produces some conflict which in turn increases arousal.
The greater the conflict, the greater the arousal. According to Berlyne,
moderate increases in arousal (or decrements if the animal is already highly
aroused) activate a reward system. This system underlies learning based
on approach responses and pleasant feelings (e.g., positive reinforcement,
appetitive classical conditioning). Thus an animal will seek out and be
rewarded by a stimulus which produces a moderate increase in its arousal.
High increases in arousal activate an aversion system which underlies learning
based on avoidance responses and displeasure (e.g., punishment, defensive
classical conditioning). Too much conflict and too much arousal create aversion
and will be avoided. Activation of the aversion system is assumed to inhibit
the reward system.

On a practical
level, complexity theory might be helpful in many disparate areas. For example,
in education it might help us to match the complexity of material to be
learned with the student’s optimal complexity level. Or workers in a plant
might be shifted among various positions in order to maintain sufficient
complexity for optimal performance.

Having seen some
possible ways in which learning and perception interact to affect information
entering the system and how it is interpreted, we turn in the next chapter
to a discussion of the possible stages the information goes through while
being processed.

SUMMARY

Man does not passively
perceive his environment. Rather he selects, filters, and interprets environmental
stimuli, largely on the basis of his past learning. Thus man’s subjective
perception of everything from simple objects and visual illusions to complex
social interactions is based on the interplay between his sensory-perceptual
mechanisms and what he has already learned. Even what man attends to is
partly determined by learning. On a broader level is the concept of set
— a
predisposition to perceive and respond to stimuli in specific ways. Set
is influenced by a variety of factors, including past experiences, motives,
context, rewards, and instructions.

An important and
unresolved question is whether learning ever affects the actual
initial perception of an object or if it affects only the responses made
to the object. The Whorfian
hypothesis suggests that
the way a person perceives his environment is molded by the nature of the
language he has learned. But do people with different languages actually
see environmental objects differently or do they merely respond to the objects
differently or process the information differently?

To a large extent
people must learn how to perceive, which is called perceptual learning. There are basically two categories of theories
of perceptual learning. The first category, which includes Gibson’s perceptual differentiation theory, assumes that the environment supplies most of
the needed information and that we must learn how to use this information.
The second category, which includes transactional
theory, assumes that
the environment supplies inadequate information, and thus we must learn
to make extrapolations from this information.

In verbal learning
studies it has been shown that it is easier to learn associations to words
that elicit a number of images than to words that elicit fewer images. Also,
the way a person codes the stimuli he is perceiving affects the ease and
nature of associations he learns to the stimuli.

Animals, including
humans, strive for variety, novelty, and complexity in their environment,
even though such striving does not necessarily satisfy any biological need.
The complexity of stimuli is assumed to affect the animal’s arousal — the
amount of general excitation. Theorists differ on how much arousal they
believe an animal will seek out. Minimum arousal theorists assume that the
less arousal there is, the better. Arousal induction theorists, on the other
hand, emphasize how stimuli that elicit arousal have a greater determining
effect on the animal’s behavior. Finally, there are the optimal arousal
theorists, who assume that the animal tries to maintain some intermediate
optimal amount of arousal.